Comparison of bacterial diversity in Bactrocera cucurbitae (Coquillett) ovaries and eggs based on 16S rRNA sequencing

Next-generation sequencing allows for fine-scale studies of microbial communities. Herein, 16S ribosomal RNA high-throughput sequencing was used to identify, classify, and predict the functions of the bacterial communities in the eggs and ovaries of Bactrocera cucurbitae (Coquillett) (Diptera: Tephritidae), which is a pest that infests a variety of cucurbit fruits at different developmental stages. Taxonomic analyses indicate that bacteria associated with B. cucurbitae represent 19 phyla, which were spread across different developmental stages. Specifically, the egg microbiota had a higher alpha diversity than those of microbiota in the primary and mature ovaries. Significant differences were not observed between the primary and mature ovaries in terms of their microbiota’s alpha diversities. Pseudomonadota, Deinococcota, Bacteroidota, Bacillota, and Actinomycetota were the dominant phyla in all three developmental stages of B. cucurbitae, and Pseudomonadaceae and Enterobacteriaceae were the most abundant families. Owing to the unique physiological environment of the ovaries, the diversity of their bacterial community was significantly lower than that in the eggs. This study provides new insights into the structure and abundance of the microbiota in B. cucurbitae at different developmental stages and contributes to forming management strategies for this pest.


Results
Analysis of taxa. We sequenced bacteria from the eggs, primary ovaries, and mature ovaries of B. cucurbitae and obtained 2,093,839 trimmed paired reads (Supplementary Table S1). The primer fragment of the QIIME cutadapt trim-paired excision sequence was called first, and the unmatched primers were discarded. DADA2 was called for quality control, denoising, splicing, and removal by QIIME DADA2 denoise pairing. The ranges of the Chao1, Simpson, and Shannon indices and the observed species were 175.912-782. 66, 0.21922-0.955111, 1.13841-6.27746, and 0.21922-0.955111, respectively (Supplementary Table S2). Figure 1 shows a boxplot of the data. One-way analysis of variance (ANOVA) indicates that the alpha diversity indices differed significantly (P < 0.001) among the eggs, primary ovaries, and mature ovaries. However, the alpha diversity index did not differ markedly (P > 0.05) between the primary and mature ovaries (Supplementary Table S3). A total of 19 bacterial phyla, 39 classes, 73 orders, 151 families, 287 genera, and 376 species were identified in the eggs, primary ovaries, and mature ovaries of B. cucurbitae ( Table 1). The rarefaction curves of all samples almost reached the saturation plateau ( Fig. 2A), indicating that the sequencing results are sufficient to reflect the diversity contained in the current samples. Continuing to increase the sequencing depth did not increase the number of new undiscovered amplicon sequence variants (ASVs). The abundance grade curve (Fig. 2B) for all samples was almost straight, which reflects the small differences in abundance between ASVs in the community.
Comparisons among the microbial communities in eggs, primary ovaries, and mature ovaries. The eggs exhibited a higher microbial diversity than those of the primary and mature ovaries. However, there were few differences between the microbial communities of the primary and mature ovaries. The major genera were Pseudomonadaceae, Providencia, and other bacteria (Fig. 3). To detect more species groups and compare them among the samples, community composition analysis was performed using heat maps. The 20 most abundant genera were selected for sorting (Fig. 5). Pseudomonadaceae, Acinetobacter, Providencia, and Enterobacter were observed consistently in all three B. cucurbitae sample types (Supplementary Table S5). Principal coordinate analysis (PCoA) based on the Bray-Curtis distance and weighted UniFrac distance was used to compare the community similarities between samples. The PCoA scatter plot indicates that two characteristic values contributed to the largest differences between the samples, which had degrees of influence of 42.2% and 20.7% based on the weighted UniFrac distance (Fig. 6A), and 44.4% and 14.1% based on the Bray-Curtis distance, This suggests that the microbial community in the eggs is unique compared with those in the ovaries at different developmental stages (Fig. 6B). PERMANOVA analysis indicates that there were significant differences between the egg and ovary microbiota of B. cucurbitae (Table 2; Egg × OVI PERMANOVA: p = 0.003; Egg × OVII PERMANOVA: p = 0.003). However, there were no significant differences between the ovary microbiota during different developmental stages ( Table 2; OVI × OVII PERMANOVA: p = 0.36).

Discussion
Traditional isolation and culturing methods have provided limited information regarding bacteria in the melon fruit fly and other species 44 . Herein, we characterized the bacterial community in the eggs, primary ovaries, and mature ovaries of the melon fruit fly, B. cucurbitae. We used MiSeq sequencing, which allowed the microbial diversity to be investigated more than would be possible using conventional methods, to report the variations in microbial communities during different developmental stages. Pseudomonadota was the most abundant bacterial phylum in the eggs, primary ovaries, and mature ovaries of B. cucurbitae. The findings are similar to g g_ __ _W Wa au ut te er rs si ie el ll la a c c_ __ _B Be et ta ap pr ro ot te eo ob ba ac ct te er ri ia a o o_ __ _B Bu ur rk kh ho ol ld de er ri ia al le es s o o_ __ _L La ac ct to ob ba ac ci il ll la al le es s f f_ __ _C Co om ma am mo on na ad da ac ce ea ae e o o_ __ _X Xa an nt th ho om mo on na ad da al le es s f f_ __ _X Xa an nt th ho om mo on na ad da ac ce ea ae e g g_ __ _S St te en no ot tr ro op ph ho om mo on na as s f f_ __ _E En nt te er ro oc co oc cc ca ac ce ea ae e g g_ __ _E En nt te er ro oc co oc cc cu us s g g_ __ _C Co om ma am mo on na as s f f_ __ _O Oc ce ea an no os sp pi ir ri il ll la ac ce ea ae e f f_ __ _S St tr re ep pt to oc co oc cc ca ac ce ea ae e g g_ __ _L La ac ct to oc co oc cc cu us s g g_ __ _N Ni it tr ro ob ba ac ct te er ri ia a f f_ __ _M Mi ic cr ro oc co oc cc ca ac ce ea ae e g g_ __ _S Sp ph hi in ng go ob ba ac ct te er ri iu um m g g_ __ _A Ag gr ro ob ba ac ct te er ri iu um m f f_ __ _S Sp ph hi in ng go ob ba ac ct te er ri ia ac ce ea ae e o o_ __ _S Sp ph hi in ng go ob ba ac ct te er ri ia al le es s c c_ __ _S Sp ph hi in ng go ob ba ac ct te er ri ii ia a g g_ __ _C Ch hr ry ys se eo ob ba ac ct te er ri iu um m f f_ __ _R Rh hi iz zo ob bi ia ac ce ea ae e g g_ __ _L La am mp pr ro op pe ed di ia a o o_ __ _O Oc ce ea an no os sp pi ir ri il ll la al le es s o o_ __ _B Bd de el ll lo ov vi ib br ri io on na al le es s g g_ __ _P Pe ed di io oc co oc cc cu us s c c_ __ _C Ch hl lo or ro op pl la as st t o o_ __ _S St tr re ep pt to op ph hy yt ta a p p_ __ _C Cy ya an no ob ba ac ct te er ri ia a g g_ __ _S Se er rr ra at ti ia a f f_ __ _A Ae er ro om mo on na ad da ac ce ea ae e g g_ __ _A Ae er ro om mo on na as s o o_ __ _A Ae er ro om mo on na ad da al le es s f f_ __ _B Ba ac ct te er ri io ov vo or ra ac ca ac ce ea ae e g g_ __ _H Ha af fn ni ia a g g_ __ _S Sa ac cc ch ha ar ri ib ba ac ci il ll lu us s o o_ __ _R Ri ic ck ke et tt ts si ia al le es s f f_ __ _m mi it to oc ch ho on nd dr ri ia a g g_ __ _P Ps se eu ud do oc ch hr ro ob ba ac ct tr ru um m g g_ __ _P Pr ro ov vi id de en nc ci ia a c c_ __ _A Ac ct ti in no ob ba ac ct te er ri ia a o o_ __ _A Ac ct ti in no om my yc ce et ta al le es s f f_ __ _B Ba ac ci il ll la ac ce ea ae e f f_ __ _S Sp po or ro ol la ac ct to ob ba ac ci il ll la ac ce ea ae e g g_ __ _S Sp po or ro ol la ac ct to ob ba ac ci il ll lu us s f f_ __ _R Ru um mi in no oc co oc cc ca ac ce ea ae e g g_ __ _M My yc co ob ba ac ct te er ri iu um m f f_ __ _M My yc co ob ba ac ct te er ri ia ac ce ea ae e f f_ __ _B Ba ac ci il ll la ac ce ea ae e_ _g g_ __ _B Ba ac ci il ll lu us s g g_ __ _R Ru um mi in no oc co oc cc cu us s g g_ __ _P Pr ra au us se er re el ll la a f f_ __ _P Ps se eu ud do on no oc ca ar rd di ia ac ce ea ae e c c_ __ _C Cl lo os st tr ri id di ia a o o_ __ _C Cl lo os st tr ri id di ia al le es s g g_ __ _B Br re ev vi ib ba ac ci il ll lu us s f f_ __ _S S2 24 4_ _7 7 g g_ __ _C Cl lo os st tr ri id di iu um m g g_ __ _C Cr ro on no ob ba ac ct te er r  The ordinate is taxa with significant differences between the groups, and the abscissa visually displays the LDA logarithmic scores for each taxon in a bar chart. Taxa are sorted by the size of the score to describe their specificity in the sample grouping. Longer lengths indicate more significant differences between the taxa, and the colors of the bars indicate the most abundant sample grouping for those taxa.  23,26,45,46 .
In this study, the developmental stage significantly affected the melon fly microbial diversity index. Chao1, observed-species, Simpson, and Shannon values, indicating that the microbial community richness varied during different developmental stages. Egg microbial richness was significantly higher than that of the ovaries, which may be the result of the unique physiological environment and function of the ovaries. Microbial diversity is also www.nature.com/scientificreports/ generally associated with food and environmental factors. Therefore, further studies are required to investigate the microbial diversity of melon flies across different hosts and geographical populations to more comprehensively determine the bacterial diversity. Pseudomonadota was the predominant phylum in the B. cucurbitae eggs and ovaries and accounted for 98.83% of the microbiota in the mature ovaries. Similar findings have been obtained for other insects, including B. minax (Enderlein) 47 , C. capitata (Wiedemann) 48 , Lutzomyia longipalpis 49 , Schistocerca gregaria 50 , Acyrthosiphon pisum 51 , Aphis fabae 52 , and Riptortus clavatus 23 . In contrast, Drosophila spp. harbor bacteria belonging to Pseudomonadota and Bacillota, and the dominant commensal bacteria in Musca domestica belong to Bacillota and Bacteroides 53,54 . The major symbiotic bacteria in termites belong to Bacteroides, Bacillota, and Spirochaetes 55,56 , whereas the dominant bacterial symbionts in Lymantria dispar, Helicoverpa armigera, Bombyx mori, and Plutella xylostella belong primarily to Bacillota. The most abundant symbiotic bacteria in Holotrichia glabripennis belong to Pseudomonadota and Actinomycetes 57 . The diversity of bacterial symbionts in insects varies with diet and environmental factors 43 . Therefore, further investigation is necessary to determine the microbial diversities in the eggs and ovaries of different populations of B. cucurbitae. Of the bacterial genera identified throughout the developmental stages of B. cucurbitae, Pseudomonadaceae was the most abundant genus. This result was not in agreement with previous findings obtained for fruit fly microflora, which indicated that Klebsiella and Citrobacter were the most abundant genera 23 . For C. capitata, the most abundant bacterial genera were Klebsiella, Pantoea, Enterobacter, Pectinobacter, and Citrobacter 51 . The functions of these bacteria are worth examining. Detecting bacteria or specific genes related to the reproductive development of B. cucurbitae can also aid the development of microbially based control strategies.
Pseudomonas was the abundant bacterial genus in the eggs, primary ovaries, and especially the mature ovaries of B. cucurbitae (≥ 72.02%). In contrast, Klebsiella and Citrobacter are the main bacterial genera in B. minax (Enderlein). The most abundant symbiotic genera in C. capitata (Wiedemann) were Klebsiella, Pantoea, Enterobacter, Pectobacterium, and Citrobacter 51 . These symbionts might be implicated in the reproduction, growth, development, and biosynthesis of B. cucurbitae. Certain Pseudomonas spp. are harmful to insects, such as Pseudomonas fluorescens, which kills mosquitoes and Musca domestica L. 58 . Pseudomonas aeruginosa is pathogenic to Caenorhabditis elegans, Drosophila spp., and Hylesia metabus larvae 59,60 and shortens the lifespan of C. capitata 51 . However, certain Pseudomonas spp. are beneficial to insects, such as P. aeruginosa, which resists parasites in mosquitoes 61 , and a Pseudomonas species that produces the anti-tumor polysterin pederin in Paederus fuscipes 62 . Another Pseudomonas species is antagonistic to certain entomopathogenic fungi 63 . Further research is therefore needed to explore the physiological functions of Pseudomonas spp. in B. cucurbitae.
Previous studies demonstrated that the community composition of insect gut bacteria, mostly dominated by members belonging to phyla Pseudomonadota and Bacillota, followed by Bacteroidota , Actinomycetota, and Tenericutes, differs owing to variation in host location, food, developmental stage, physiology, and phylogeny 64 . Similarly, our results indicated that Pseudomonadota is the main phylum identified among the insect gut bacteria, and hence, the microbes present in the gut can be transmitted to the ovary and play an important role. Previous research detected that the Enterobacteriaceae community in the gut of medflies may indirectly contribute to host fitness by preventing the establishment or proliferation of pathogenic bacteria. Previously, Eyal Ben Ami demonstrated that the Klebsiella oxytoca-supplemented diets significantly shortened the mating latency of the sterile male Mediterranean fruit flies. Romero et al. demonstrated that Citrobacter freundii stimulated oviposition to its greatest extent and also supported stable fly development 22 . Gut bacteria that have vital roles in insect growth and development can be transferred from one generation to the next through infected ovaries. Candidatus Riesia sp. is localized in the gut of Pediculus humanus during the female development. The symbiont bacteria exit through a hole of the bacteriocytes and move posteriorly along the surface of the gut to the lateral fallopian tube, and subsequently, they reach and aggregate in the side fallopian tube and eggs 65 .
To date, several important Drosophila management strategies have been developed to control cash crops. Symbiodinium has become an important weapon for controlling Drosophila, For example, Klebsiella oxytoca, K. pneumonia, Enterobacter cloacae, Pantoea agglomerans, Citrobacter sp., Providencia sp., Raoultella terrigena, and Bacillus cereus have acted as attractants to control B. dorsalis, Z. tau, B. cucurbitae, and B. zonata. In addition, bacteria contribute substantially to SIT by increasing the calls, life expectancies, and mating abilities of mass-reared C. capitata male adult flies. Symbiotic bacteria also influence the mass rearing of parasitoids, as host detection has suggested that the combination of attractancy, SIT, and parasitoid application could be a suitable technique for controlling fruit fly species 42,63 . The present study provides new insights into the microbial community structure and its abundance during the different developmental stages of B. cucurbitae, and provides a theoretical basis for developing pest management strategies. The variations in the bacterial community throughout the developmental stages of B. cucurbitae may be the result of their habitat during these life stages. In addition to habitat, the transmission patterns of different bacterial species may affect their presence/absence during different life stages. The differences in the relative bacterial abundances in B. cucurbitae observed in this study may also be the result of geographical isolation. The diversity and abundance of the bacterial community can also vary with pH, partial oxygen pressure, and the presence/absence of physical barriers.
In summary, this is the first study to characterize the bacterial diversity, abundance, and functional predictions associated with the eggs and ovaries of B. cucurbitae using Illumina-based sequencing. We found that the alpha diversity indices differed significantly between the eggs and ovaries, but not between the primary and mature ovaries of B. cucurbitae. Overlapping bacterial taxa were detected in the ovaries and eggs. These taxa could have important roles in the growth and development of the host insect. The main roles of the commensal bacteria in B. cucurbitae included biosynthesis, degradation/utilization/assimilation, and precursor metabolite and energy generation. Further experiments should be performed to determine the functions of these bacteria. Overall, the findings of the present study provide a fine-scale understanding of the bacterial diversity in B. cucurbitae, which can be used to develop novel pest management strategies. Egg collection. Eight days after adult emergence, bacterially uninfected cucumber slices were placed in each cage for adults to lay eggs. After 12 h, the cucumber slices were removed from the cage and the eggs were collected with a fine brush. The pooled egg samples were then washed with sterile ddH 2 O on a UV-sterilized workbench (AIRTECH JAPAN LTD., Tokyo, Japan) for 30 min. Finally, the eggs were stored in a 1.5 mL sterile tube at −80 °C for subsequent use. Two hundred eggs were pooled into one biological sample, and a total of six biological samples were obtained.
Ovarian anatomy. Ovaries were dissected from emerged adults (matured in a home cage) on days 1 (primary ovaries) and 8 (mature ovaries The PCR products were examined on 1.5% agarose gel and purified using a QIAGEN Gel Extraction Kit (Qiagen, Dusseldorf, Germany). The purified products were sequenced using an Illumina MiSeq platform by Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China). We performed 2 × 250 bp two-end sequencing using the MiSeq Reagent Kit V3 (600 cycles). After filtering out low quality or ambiguous reads and removing chimeric sequences, the high-quality sequences were clustered into ASVs at 100% similarity, and bacterial taxonomy was phylogenetically assigned using the Ribosomal Database Project classifier 66 .
Data analysis. Microbiome information was analyzed using QIIME2 2019.4, with the processes modified and refined using official tutorial 4 (https:// docs. qiime2. org/ 2019.4/ tutor ials/). Raw sequence data were processed using the demux plugin for decoding and cutadapt for primer excision. The data were then processed (i.e., quality filtering, denoising, and chimera removal) using the DADA2 plugin. The obtained sequences were merged based on 100% sequence similarity, and characteristic ASVs and abundance data tables were generated.
Taxonomic composition analysis. The composition and abundance distribution tables for each sample at the phylum, family, and genus levels were obtained using the RStudio software package. Based on the comprehensive data, the 10 most abundant of each group were selected for analysis.
Alpha diversity index analysis. First, using the QIIME2 software package, the total number of sequences for each sample in the ASV abundance matrix differed. Random sampling under depth and sparse curves were plotted with the number of sequences drawn at each depth and their corresponding ASV number to determine whether the sequencing depth of each sample was sufficient to reflect the microorganisms contained in the community sample diversity. Second, to compare the diversities of different samples to the sample with the fewest sequences in the whole sample, 95% of the sequences were randomly drawn in the ASV abundance matrix to correct the sequencing depth diversity differences. Subsequently, the Chao1, observed species, Shannon, and Simpson diversity indices were calculated separately for each sample using QIIME2. A boxplot was created to compare the richnesses and evenness of the The alpha diversity and relative abundance data were analyzed using one-way analysis of variance (ANOVA) with SPSS 26.0 (IBM SPSS Statistics), and multiple comparisons were analyzed using Tukey's test.
Beta diversity analysis. Beta diversity analysis was performed using the weighted UniFrac and Bray-Curtis distance metrics to investigate changes in the microbial community structure among the samples with the R and QIIME2 software packages, and was visualized using the PCoA method.